(2) PCNN [1]}: Words are represented using word embeddings and two positional embeddings. A convolutional neural network (CNN) is applied to the word representations. Rather than applying a global max-pooling operation on the entire sentence, three max-pooling operations are applied on three segments of the sentence based on the location of the two entities (hence the name Piecewise Convolutional Neural Network (PCNN)). The first max-pooling operation is applied from the beginning of the sentence to the end of the entity appearing first in the sentence. The second max-pooling operation is applied from the beginning of the entity appearing first in the sentence to the end of the entity appearing second in the sentence. The third max-pooling operation is applied from the beginning of the entity appearing second in the sentence to the end of the sentence. Max-pooled features are concatenated and passed to a feed-forward layer with softmax to determine the relation.
